DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training

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DiskGNN: Bridging I/O Efficiency and Model Accuracy for

Out-of-Core GNN Training

Renjie Liu

1,4,∗

, Yichuan Wang

2,∗

, Xiao Yan

, Zhenkun Cai

, Minjie Wang

, Haitian Jiang

Bo Tang

, Jinyang Li

1Southern University of Science and Technology

2Shanghai Jiao Tong University

3Centre for Perceptual and Interactive Intelligence

4AWS Shanghai AI Lab

5New York University

ABSTRACT

Graph neural networks (GNNs) are machine learning models spe-

cialized for graph data and widely used in many applications. To

train GNNs on large graphs that exceed CPU memory, several

systems store data on disk and conduct out-of-core processing.

However, these systems suffer from either read amplification when

reading node features that are usually smaller than a disk page or

degraded model accuracy by treating the graph as disconnected par-

titions. To close this gap, we build a system called DiskGNN, which

achieves high I/O efficiency and thus fast training without hurting

model accuracy. The key technique used by DiskGNN is offline

sampling, which helps decouple graph sampling from model com-

putation. In particular, by conducting graph sampling beforehand,

DiskGNN acquires the node features that will be accessed by model

computation, and such information is utilized to pack the target

node features contiguously on disk to avoid read amplification. Be-

sides, DiskGNN also adopts designs including four-level feature store

to fully utilize the memory hierarchy to cache node features and re-

duce disk access, batched packing to accelerate the feature packing

process, and pipelined training to overlap disk access with other

operations. We compare DiskGNN with Ginex and MariusGNN,

which are state-of-the-art systems for out-of-core GNN training.

The results show that DiskGNN can speed up the baselines by over

8x while matching their best model accuracy.

1 INTRODUCTION

Graph data is ubiquitous in domains such as e-commerce [1], fi-

nance [2, 3], bio-informatics [4], and social networks [5]. As ma-

chine learning models specialized for graph data, graph neural net-

works (GNNs) achieve high accuracy for various graph tasks (e.g.,

node classification [6], link prediction [7], and graph clustering [8])

and hence are used in many applications like recommendation [9],

fraud detection [10], and pharmacy [11]. In particular, GNNs com-

pute an embedding for each node 𝑣 in the graph by recursively

aggregating the input features of 𝑣’s neighbors. For large graphs,

to reduce the cost of aggregating all neighbors, graph sampling is

usually adopted to sample some of the neighbors for aggregation.

Large graphs with millions of nodes and billions of edges are

common in practice [12–14]. They may not fit in the main mem-

ory of a single machine and require solutions to scale up GNN

training. Some systems (e.g., DistDGL [15], DSP [16], and P3 [17])

conduct distributed training across multiple machines, by partition-

ing the graph data and training computation over the machines.

However, distributed solutions are expensive to deploy and suffer

∗Renjie and Yichuan contribute equally.

Table 1: Execution statistics of two disk-based GNN training

systems and our DiskGNN on the Ogbn-papers100M graph.

The CPU memory is set as 10% of the dataset size.

Metrics

Ginex MariusGNN DiskGNN

[18]

[20]

(ours)

Avg. epoch time (sec)

580

165

76.3

Disk access time (sec)

412

27.1

51.2

Disk access volume (GB)

484

6.46

73.9

Test accuracy (%)

65.9

64.0

65.9

from low GPU utilization due to the heavy inter-machine communi-

cation required to handle the cross-partition edges. Observing that

solid-state disks (SSDs) are now cheap, capacious, and reasonably

fast (with a bandwidth of 2-7GB/s), some systems (e.g., Ginex [18],

GIDS [19], MariusGNN [20], and Helios [21]) conduct out-of-core

training by storing the graph data on the disk of a single machine.

Compared with distributed systems, these disk-based solutions are

more accessible and cost effective.

Existing disk-based systems and their limitations. Most ex-

isting disk-based systems, including Ginex [18], GIDS [19] and He-

lios [21]), adopt the same workflow as in-memory training systems.

Specifically, for each mini-batch training step, they first perform

graph sampling to determine which node features are needed, fetch

those features, and then perform the batch’s model computation; the

difference is that on-disk systems read node features from the disk

instead of CPU memory. Despite various system optimizations, such

as CPU memory caching [18] and concurrent disk reads [19, 21],

a fundamental problem with these systems lies in their random

small reads pattern, whereby only a single node’s feature (typically

< 512 bytes) is used for each disk read at 4KB page granularity,

causing substantial read amplification and poor I/O efficiency. As

shown in Table 1, Ginex spends most of its training time on disk

access, and its disk read volume (484GB) is much larger than the

total amount of sampled node features (73.9GB, as achieved by our

system DiskGNN) under the same cache size constraint.

To avoid read amplification, MariusGNN [20] pre-processes the

graph to enable more efficient large reads during training. Specif-

ically, MariusGNN stores the graph in a set of partitions on disk

and swaps in a few of them to CPU memory during each training

epoch. Sampling of mini-batches is done on the sub-graph from all

memory-resident partitions. Although MariusGNN achieves effi-

cient disk I/O, its mini-batch sampling is quite biased as it ignores

arXiv:2405.05231v1 [cs.LG] 8 May 2024

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nodes from any non-memory-resident partition, thereby resulting

in degraded model accuracy as shown in Table 1.

Our system DiskGNN. To eliminate the aforementioned tension

between I/O efficiency and model accuracy, we build a new out-

of-core GNN system (DiskGNN) based on an alternative training

paradigm called offline sampling. With offline sampling, DiskGNN

decouples the two main stages of GNN training, i.e., graph sampling

and model training, and conducts graph sampling for many mini-

batches before model computation. In this way, offline sampling

can determine the node features that will be accessed during model

computation and use the information to optimize the data layout

proactively for efficient access. Specifically, we group the node fea-

tures according to their access frequencies and assign them to a

four-level feature store that involves GPU memory, CPU memory,

and disk, with the principle of caching more popular node features

in faster storage. More importantly, to avoid read amplification, we

pre-process the node features that each mini-batch needs to read

from disk by packing them into a consecutive disk region. As naive

packing may result in very large storage overhead, we trade off

between I/O efficiency and disk space with a hybrid packing strat-

egy, which consists of shared features among mini-batches that use

node reordering to reduce read amplification and dedicated features

for each mini-batch that use consecutive packing. We also make the

pre-processing and packing efficient using batched packing, which

sequentially reads large chunks of node features and writes back

the required node features for all mini-batches in one pass. Besides

out-of-core training, we also discuss the potential benefits of offline

sampling for other scenarios.

We implement DiskGNN on top of the Deep Graph Library

(DGL) [22], one of the most popular open-source frameworks for

deep learning on graphs. DiskGNN adopts a training pipeline to

overlap the disk access of a mini-batch with the model computation

of the previous mini-batches. We also carefully implement the I/O

operations of DiskGNN for efficiency and provide simple APIs for

usability.

We evaluate DiskGNN on 4 large public graph datasets and

2 predominate GNN model architectures. The results show that

DiskGNN consistently yields shorter training time than both Ginex

and MarisGNN with an average speedup of 7.5x and 2.5x over the

two of them, respectively. Moreover, DiskGNN matches the model

accuracy of Ginex while being significantly more accurate than

MarisGNN. We also conduct micro experiments to validate the

effectiveness of our designs. The results show that the batched

read-write can accelerate pre-processing by 7.3x, and the training

pipeline can speed up training by over 2x.

To summarize, make the following contributions in this paper.

• We observe that existing disk-based GNN training systems face

the tension between I/O efficiency and model accuracy and that

they cannot achieve both simultaneously.

• We design DiskGNN to achieve both I/O efficiency and model

accuracy by exploiting offline sampling, which collects the data

access beforehand to optimize the data layout for access.

• We propose a suite of designs tailored for on-disk workloads to

make DiskGNN efficient, which include four-level feature store,

batched feature packing, and pipelined training.

V+

Sampled Nodes

V,,

Disk Feature Layout

In Pages

F+ F,

F. F/

F0 F1

F2 F3

F4 F5

F,+F,,

Data Graph

Figure 1: An illustration for node-wise graph sampling. The

seed node is 𝑣0, and the sampled 1-hop and 2-hop neighbors

are marked in yellow and green, respectively. We assume

that two node features take up a disk page.

2 BACKGROUND ON GNN TRAINING

GNN basics. GNN models usually take a data graph 𝐺 = (𝑉,𝐸),

where 𝑉 and 𝐸 are the node set and edge set, and each node 𝑣 ∈ 𝑉

comes with a feature vector ℎ0

𝑣 that describes its properties. For

instance, in the Ogbn-papers100M dataset, each node is a paper, an

edge indicates that one paper cites another paper, and each node

feature vector is a 128-dimension float embedding of a paper’s title

and abstract. A GNN model typically stacks multiple graph aggrega-

tion layers with each layer aggregating the embeddings of a node’s

neighbors to compute an embedding for the node. Specifically, in

the 𝑘th layer, the output embedding ℎ𝑘

𝑣 of node 𝑣 is computed as

ℎ

𝑘

𝑣 = 𝜎 [𝑊𝑘 · 𝐴𝐺𝐺𝑘({ℎ𝑘−1

𝑢

,∀𝑢 ∈ N(𝑣)})],

(1)

where set N(𝑣) contains the neighbors of node 𝑣 in the graph, and

ℎ𝑘−1

𝑢

is the embedding of node 𝑢 in the (𝑘 − 1)th layer. For the first

layer, ℎ0

𝑢 is the input node feature vector. 𝐴𝐺𝐺𝑘 (·) is the neighbor

aggregation function, and typical choices include mean, max, sum,

and concatenate.𝑊𝑘 is a projection matrix of size 𝑑′ ×𝑑, where 𝑑′

is the dimension of ℎ𝑘

𝑣 and 𝑑 is the dimension of ℎ𝑘−1

𝑢

. 𝜎(·) is the

activation function. By expanding Eq. (1), it can be observed that

for a 𝐾-layer GNN model, computing the final output embedding

ℎ𝐾

𝑣 for node 𝑣 involves its 𝐾-hop neighbors.

Graph sampling for GNN training. GNN training is usually con-

ducted in mini-batches with each mini-batch computing the output

embeddings for some seed nodes (i.e., the nodes whose labels are

known) and updating the model according to a loss function that

measures the difference between model output and ground-truth.

Training is said to finish an epoch when all seed nodes are used

once, and typically many epochs are required for the model to

converge. As a seed node can have many 𝐾-hop neighbors, graph

sampling is widely used to reduce training cost by sampling some

of the neighbors for computation [23–28]. For instance, the pop-

ular node-wise sampling [23] uses a fan-out vector to specify the

number of neighbors to sample and conducts neighbor sampling

independently for the nodes in the same layer. For instance, the left

plot of Figure 1 uses a fanout of <2,2>, which means that sampling

is conducted for 2 steps, and each node samples 2 neighbors for

both steps. In the first step, {𝑣3,𝑣5} are sampled as the neighbors

of the seed node 𝑣0; in the second step, 𝑣3 samples its neighbors

{𝑣2,𝑣7} while 𝑣5 samples {𝑣9,𝑣11}.

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3 OFFLINE SAMPLING

Challenge for out-of-core training. For graph datasets, the node

features are usually (much) larger than the graph topology, and

thus out-of-core training needs to store (most of) the node features

on disk. For each mini-batch, the related node features need to be

fetched from the disk to GPU memory to conduct model computa-

tion. For instance, in Figure 1, handling seed node 𝑣0 requires the

node features of {𝑣0,𝑣3,𝑣5,𝑣2,𝑣7,𝑣9,𝑣11}. However, disk is accessed

with page size as minimum granularity (usually 4KB), and each

node feature is usually much smaller than disk page size (e.g., the

128-dimension float vector of the Ogbn-papers100M dataset takes

512 bytes). As the required node features are not contiguous on

disk, many small random reads are used, which cause read ampli-

fication and make the disk traffic for feature reading much larger

than the required features (i.e., see Ginex in Table 1), yielding low

I/O efficiency. For instance, in the right plot of Figure 1, we assume

that two node features take up a disk page; training only needs to

read 7 node features but the total disk traffic is 12 node features.

Offline sampling for data layout optimization. Each mini-

batch of GNN training involves two main steps, i.e., graph sampling

to determine the computation graph and the node features to use,

and model computation to compute output embeddings for the seed

nodes and update the model according to the loss function. Existing

systems for GNN training couple the two steps of a mini-batch, i.e.,

they run the mini-batches sequentially, and each mini-batch first

conducts graph sampling and then model computation. The insight

of offline sampling is that we do not have to perform model compu-

tation immediately after finishing graph sampling for a mini-batch;

instead, we can sample many mini-batches before model computa-

tion. By decoupling graph sampling and model computation, offline

sampling collects the features to be accessed beforehand, which

enables the following opportunities to optimize the data layout for

efficient access during model computation.

• Cache configuration to reduce disk access. With the node features

to be accessed by all mini-batches, we can rank the nodes by their

access frequencies and cache more popular nodes in faster mem-

ory (e.g., GPU memory and CPU memory) to reduce disk access.

Such a cache configuration is also optimal in that it minimizes

the total number of node features fetched from the disk.

• Feature packing to avoid read amplification. For each mini-batch,

we can first collect all node features it requires and store them

contiguous as a large disk block (i.e., feature packing). During

model computation, we can read all these features as a single

disk block without read amplification.

• Batched packing for many mini-batches. If we conduct feature

packing individually for each mini-batch, it will be inefficient

because small random disk read is still needed to collect the

required node features. However, when handling a large number

of mini-batches, most of the node features are accessed by at least

one of these mini-batches. This observation allows us to switch

the feature packing scheme from mini-batch oriented to feature

partition oriented. In particular, we can read a large partition

of node features from disk each time, find the features in the

partition that are required by each mini-batch, and append these

features to the disk storage of each mini-batch. This multi-batch

packing is efficient as it involves only large sequential disk access.

Our DiskGNN exploits all the above optimization opportunities

enabled by offline sampling with system designs. We note that

feature packing essentially trades disk space for access efficiency

because a node feature may be required by multiple mini-batches

and thus replicated multiple times by packing. The increased disk

space consumption is usually not a problem because disk capacity

is cheap. When the space overhead of packing is too large, offline

sampling may use node reordering [29, 30], a classical technique in

graph processing, to reduce read amplification. The rationale is to

renumber the graph nodes such that the node features accessed

concurrently by the mini-batches are likely to be in the same disk

page. Besides, offline sampling can store the graph samples and

packed node features, which may be reused multiple times to save

sampling and packing costs. It has been shown that reusing graph

samples does not harm model accuracy [31].

In addition to disk-based training, offline sampling may also ben-

efit cloud-based training by allowing flexible instance selection. This

is because public clouds (e.g., AWS [32] and Azure [33]) provide

machine instances with different configurations (e.g., memory ca-

pacity, CPU power, with or without GPU) and thus different prices;

and with graph sampling decoupled from model computation, we

can choose a proper instance for each of them to reduce the mone-

tary cost. In particular, graph sampling requires the graph topology

and conducts small random access, and thus it suits an instance

with enough CPU memory to hold the graph topology. We do not

need to hold the node features in CPU memory and may not use

GPU because the computation of sampling is lightweight. Model

computation involves neural networks and thus suits an instance

with GPU. We can reduce the idle time of expensive GPU by con-

ducting graph sampling and packing the node features beforehand

on a cheaper instance.

4 DISKGNN SYSTEM OVERVIEW

Figure 2 depicts the overall architecture and workflow of DiskGNN.

At initialization, DiskGNN takes the graph samples for many mini-

batches and all node features of the graph as input and assumes

that they are stored on the disk. The graph samples can be eas-

ily obtained by conducting graph sampling using existing GNN

frameworks like DGL [22] and PyG [34]. Then, DiskGNN conducts

pre-processing to construct a data layout for efficient access during

model training. This is achieved by storing the node features in

a four-level feature store, which is aimed to fully utilize the mem-

ory hierarchy and will be introduced shortly. After pre-processing,

DiskGNN runs model training by going over the mini-batches to

update the model. For each mini-batch, DiskGNN loads its graph

sample from the disk and assembles the required node features from

the four-level feature store, and the GPU uses these data to conduct

model computation. The data reading and model computation of

different mini-batches are overlapped with a training pipeline.

Note that DiskGNN can conduct pre-processing and training on

different machines for low monetary cost because pre-processing

does not require GPU. In this case, pre-processing determines the

node features to be stored in GPU and CPU memory and writes

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…

CPU

Pipeline

Queue

GNN Model

Online Training

…

Graph Samples

Original Features

Feature Dispatcher

GPU Cache

CPU Cache

…

Disk Cache

Disk Cache

Generator

Reordering

Packing

Bi-partite Graph

Pipeline

Queue

+ + +

Full Mini-batch

Partial Mini-batch

GPU

Reorganized Data Layout

Preprocessing

Original Data Layout

CPU

Input

Write

Read

CPU Cache

GPU Cache

Packed

Feature G!

On-disk Features

Figure 2: DiskGNN system architecture and workflow.

them as separate blocks on disk; the training machine loads the

two feature blocks to initialize its GPU and CPU cache.

Four-level hierarchical feature store. During pre-processing,

DiskGNN first goes over all graph samples to collect the access

frequencies of all node features. We say nodes that are accessed

more times are more popular. Then, DiskGNN determines where

to store each node feature according to its popularity as follows:

• GPU cache stores the most popular node features, and accesses to

these node features enjoy the high bandwidth of GPU memory.

The GPU cache size is configured by excluding the working

memory for model training from the total GPU memory.

• CPU cache stores the second popular node features, and the GPU

accesses the CPU cache as unified virtual memory (UVM) [35]

via PCIe. This usually does not cause read amplification because

the access granularity of UVM is 50 bytes, which is typically

smaller than a node feature. The CPU cache size is configured by

reserving the memory to assemble the graph samples and node

features of several mini-batches to prepare for training.

• Disk cache stores the third popular node features. As discussed in

§ 3, packing all node features for each mini-batch may consume

large disk space because a node feature can be stored multiple

times. DiskGNN allows the user to specify the maximum disk

space the system can use and disk cache is activated when pack-

ing exceeds the space limit. The node features in the disk cache

are not replicated, instead, they are processed by node reordering

in the hope that the node features required by each mini-batch

are stored in a small number of disk pages. As such, the disk

cache reduces rather than eliminates read amplification.

• Packed feature chunk stores the node features that are not in the

above three cache components. For each mini-batch, DiskGNN

creates a disk chunk to store its packed features, and the graph

sample of the mini-batch is also packed in the chunk as the node

features and graph samples will be fetched together. Accesses

to the packed feature chunks do not have read amplification

because each chunk is usually larger than disk page size.

Instead of activating the disk cache after observing that packing

uses too much space, DiskGNN decides whether and how to use

the disk cache by analyzing the access frequencies of the node

features. The details of determining the node features to store in

the disk cache and node reordering are discussed in § 5.1, and how

to conduct the pre-processing and fill in the four-level feature store

efficiently is introduced in § 5.2.

PI#0 PI#1 PI#2 PI#3 G#0 C#2 PI#4

V""

Address Lookup

P#0 P#1 D#1 D#0

D#5

PG#0

Merge IO

Requests

Disk Cache Fetcher

PG#2

F$ F%

F* F)

F"! F""

PG#0

PG#1

F! F&

Packed

Feature

F""

Address Lookup

Disk Cache

CPU

GPU

Disk

CPU Assembler

CPU Cache

Partial Input

GPU Assembler

UVM

Access

HBM

Access

F' F+

GPU Cache

Input NID

F""

Input Feature

F! F&

Figure 3: An example of feature assembling in DiskGNN. G#,

C#, D#, P#, and PI# denote that a feature locates in the GPU

cache, CPU cache, disk cache, packed chunk, and partial

input, respectively. PG refers to disk pages in the disk cache.

Feature assembling. During training, DiskGNN takes two steps to

assemble the node features in the feature store for each min-batch.

In the first step, the CPU reads the features in the disk cache and

packed feature chunks and prepares them as partial input for the

GPU. In the second step, the GPU reads the GPU cache, CPU cache,

and partial input to obtain all the required features. DiskGNN uses

HashMaps as address lookup tables in both steps to get knowledge

of the requested feature locations. These tables translate the node

IDs to feature addresses in the four-level feature store. Figure 3

provides a working example of the feature assembling process,

where the required node features are {0, 3, 5, 2, 7, 9, 11}, and nodes

{1, 9} and {7, 4} are stored in the CPU and GPU cache, respectively.

During CPU assembling, DiskGNN interprets the node IDs to

address their locations on disk and launches disk I/O to read their

features from the packed chunks (P#) and disk cache (D#). For the

packed features, feature chunk {0, 3} is loaded via one disk page. For

features {2, 5, 11} located in the disk cache, DiskGNN first merges

the requests pointing to the same disk page to eliminate duplicate

accesses and then reads the required pages. In particular, {2, 5}

are in the same disk page due to node ordering and thus fetched

via one page. For GPU assembling, DiskGNN interprets the node

IDs to address their locations in CPU and GPU, including partial

input (PI#), CPU cache (C#) and GPU cache (G#). To collect all input

features, DiskGNN launches UVM accesses to load the features for

PI# and C# and directly fetches features in GPU cache for G#.

Page 5

Mini-batch Input NID

F$ F( F% F* F& F) F' F+

Page#0 Page#1 Page#2 Page#3

Disk Cache

F$ F% F& F' F( F* F) F+

Page#0 Page#1 Page#2 Page#3

PG#0

Interpret

Fetch

Interpret

Fetch

Disk Cache

…

F$ F% … F( … F+

Bi-partite Graph

New Ordering

MinHash

Mini-batch Input NID

PG#0

PG#1

PG#1 PG#2 PG#3

(b) MinHash Reorder

(a) Access Before Reorder

(c) Access After Reorder

Figure 4: Feature access pattern before and after reordering.

Minhash bucketing is used to reorder features in disk cache.

Algorithm 1 Disk Cache Reordering using MinHash

Input: A set of 𝑛 graph samples {𝐺1,...,𝐺𝑛}, disk cache entries

Vd = {Vd1,..., Vdm}, number of hash functions k

Output: Reordered cached entries Vr

1: H ← ∅

2: S ← [𝑖𝑛𝑓 for range |Vd|]

3: **generate k hash functions**

4: for i ← 1,..., k do

H ← H ∪ { Permute(1,..., n) }

6: **iterate over the mini-batches**

7: for i ← 1,..., n do

(Vi, Ei) ← Gi, Vin ← Vi ∩ Vd

for v ∈ Vin do

10:

for Hj ∈ H do // calculate minhash values

11:

S(v) ← Min(S(v), Hj(i))

12: Vr ← Vd[Argsort(S)]

13: Return Vr

5 SYSTEM DESIGNS

In this section, we discuss the major design components and opti-

mizations of DiskGNN in detail. First, we describe how to organize

disk cache to trade off between read amplification and storage

overhead in §5.1. Then, we discuss optimizations to speed up fea-

ture packing for pre-processing in §5.2, followed by the pipelined

training of DiskGNN in §5.3.

5.1 Segmented Disk Cache

The goal of using a disk cache shared among all graph samples is to

reduce the high storage overhead caused by the duplication of the

packed node features for different graph samples. However, this

would bring about random disk accesses and read amplification

when fetching features from disk cache. A promising solution to

alleviate read amplification is to promote better data locality via

smart node reordering. By storing node features in the disk cache

according to a better node ordering, I/O requests to separate disk

pages can potentially be merged into the same page. In this way,

random feature read will access fewer disk pages and thus reduce

the I/O traffic. In this part, we first discuss how DiskGNN reorders

nodes on a global disk cache and analyze the limitations. Then,

we introduce the proposed method to achieve a better trade off

between disk space consumption and read amplification.

10M

Number of Disk Cache Entries

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

I/O

raffic

Amplification

No-reorder

Global

Segmented

(a) I/O reduction

200

400

600

Minibatch ID

0.1

0.25

0.4

0.55

0.7

0.85

1.0

Ratio

I/O

ver

No-reorder

Global

Segmented

(b) I/O across mini-batches

Figure 5: Effect of node reordering of global and segmented

disk cache on Friendster dataset. (a) shows I/O amplification

for an epoch and (b) shows the ratio of I/O over non-reordered

disk cache for different mini-batches.

Reordering for global disk cache. Figure 4 shows how DiskGNN

conducts node reordering to achieve better data locality on disk.

Before reordering, the required features for nodes {0, 2, 4, 6} reside

in four different disk pages and four random reads are needed to

load them. Suppose the underlying disk page can accommodate

2 node features. Then the four random reads will incur 2x read

amplification. To find a better node ordering, DiskGNN models

the nodes whose features are in the disk cache and all the graph

samples using a bi-partite graph in which a node is connected to a

graph sample if it appears in the graph sample. With this bi-partite

graph, the graph reordering method HashOrder [29] can be used

to group nodes that have the most similar neighbors (i.e., required

by similar sets of graph samples)and store them contiguously on

disk. Since each node is required by a set of graph samples, thus we

adopt a hashing method like MinHash to discover set similarities.

More complex graph reordering techniques like Gorder [30] are not

used since they are much more expensive than HashOrder which

provides comparable reordering quality.

Algorithm 1 shows how to generate a MinHash value for each

node and use it for reordering. Specifically, given 𝑛 total graph

samples, we randomly generate a permutation for 1,...,𝑛 which

will be used to calculate the hash signature for a graph sample.

For every graph sample, we incrementally update the min-wise

hash value for each of the graph sample’s nodes assigned to the

disk cache using the hash signature of the graph sample (line 11).

After iterating over all the graph samples, we obtain the MinHash

value for each disk cache node. By sorting nodes according to

their hash values, nodes that are required by the same set of graph

samples are grouped in adjacent locations. In practice, multiple hash

functions can be used to reduce the probability of hash collision,

and empirically using 2 hash functions provides good reordering

quality. By reordering, requests to node feature 0 and 2 are located

in the same page (so do 4 and 6) and can be merged into one single

disk access. Therefore, only two random reads are needed for node

features {0, 2} and {4, 6}, which cuts down I/O by 50%.

Unfortunately, reordering nodes for a global disk cache shared by

all graph samples has little impact on reducing read amplification.

Figure 5a shows the result of global reordering on the Friendster

Page 6

iterate over the mini-batches (mini-batch ID)

F𝟐

F𝟓

F𝟖

F𝟏𝟏

Original

Disk Features

F𝟎

F𝟐

F𝟔

F𝟕

F𝟎

F𝟏

F𝟐

F𝟑

F𝟒

F𝟓

F𝟔

F𝟕

F𝟖

F𝟗

F𝟏𝟎

F𝟏𝟏

iterate over the node features (chunk ID)

F𝟎

F𝟏

F𝟐

F𝟑

F𝟒

F𝟓

CPU

Concatenation Result

Disk

(a) Individual Packing

(b) Batched Packing

F𝟐

F𝟎

F𝟓

F𝟖

F𝟏𝟏

Packed Features

CPU

F𝟎

F𝟐

F𝟓

F𝟔

F𝟕

F𝟖

F𝟗

F𝟏𝟎

F𝟏𝟏

CPU

F𝟔

F𝟕

F𝟖

F𝟏𝟏

F𝟎

F𝟏

F𝟐

F𝟑

F𝟒

F𝟓

F𝟔

F𝟕

F𝟖

F𝟗

F𝟏𝟎

F𝟏𝟏

F𝟔

F𝟕

F𝟐

F𝟎

F𝟐

F𝟔

F𝟕

F𝟐

F𝟓

F𝟖

F𝟏𝟏

Disk

Original

Disk Features

…

Figure 6: Optimization of feature packing during DiskGNN’s pre-processing.

Algorithm 2 Search Method for Segmented Disk Cache

Input: Graph samples G = {G1, ..., Gn}, total nodes V, CPU cache

Vc, GPU cache Vg, disk space constraint c, feature dimension f

Output: Number of disk cache entries m, segment size s

1: count ← [0 for range |V|]

2: for Gi = (Vi, Ei) ∈ G do

𝑉𝑐𝑚 ← Vi − Vc − Vg

for v ∈ Vcm do

count[v] ← count[v] + 1

Vd, Vp ← V[count > 1], V[count = 1]

disk_space ← ((|Vd|+|Vp|) × |G|/i) × f × 4

if disk_space <= c do

Return m ← |Vd|, s ← i

dataset. The I/O traffic grows with more node entries stored in the

disk cache due to more random disk access with read amplification.

It is observed that reordering can only reduce total I/O traffic by less

than 10% in one epoch with varying disk cache sizes. We further

examine the ratio of I/O traffic over non-reordered baseline for every

mini-batch in Figure 5b. We can see that only about one-third of

the mini-batches (i.e., graph samples) have noticeable I/O reduction

from this global reordering, and only one-tenth of the mini-batches

achieve more than 50% I/O reduction. This is reasonable as the

GPU and CPU cache already store node features with high access

frequencies while the remaining nodes are shared more uniformly

among graph samples, so a single global order is unlikely to achieve

good data locality for all graph samples.

Local reordering for segmented disk cache. The observation

that a small percentage of graph samples benefit from reordering

in one global disk cache motivates us to divide the graph samples

into multiple segments and create a locally-reordered disk cache for

each segment. Specifically, given a segment size, we use the same

metric (i.e., node access frequency) to extract disk cache entries

for the graph samples and perform node reordering as introduced

before in Algorithm 1 on this disk cache. During training, each

graph sample fetches its required features from the shared disk

cache of its corresponding segment (as well as its packed feature

chunk store). Figure 5b shows that, with a locally-reordered disk

cache per 50 graph samples, the I/O traffic is significantly reduced,

compared with using a global reordered disk cache.

Approximate search method. To materialize the segmented disk

cache, we need to determine the number of disk cache entries 𝑚

and segment size 𝑠. As is discussed above, putting more features

in disk cache brings about more random disk reads, and enlarging

the segment size degrades the reordering quality (measured by I/O

traffic). On the other hand, setting a very small disk cache and

segment size results in many replicated packed features across

graph samples, consuming too much disk space. To this end, we

need to search for the sweet point of 𝑚 and 𝑠, which achieves low

read amplification while satisfying the user’s disk space constraint.

The brutal-force solution is to iterate over all possible combina-

tions of 𝑚 and 𝑠 that satisfy the disk space constraint, calculate the

I/O traffic for an epoch, and select the configuration with the lowest

I/O traffic. However, as a 2-dimensional grid search, the brutal-force

method needs to generate and reorder the segmented disk cache

repeatedly for every combination of 𝑚 and 𝑠, which is too costly.

To solve this issue, we propose an approximate method to find the

near-optimal parameters using only 1-dimensional search on the 𝑠.

Algorithm 2 describes the approximate search process. We as-

sume that the storage consumption is similar for different 𝑠 when

𝑚 is small since most of the on-disk features are assigned to the

packed feature chunks rather than disk cache. Also, the I/O traffic

will be lower for smaller 𝑠 with the same 𝑚, as good data locality

is more likely to remain with smaller groups. Based on the above

observations, we can simply set 𝑚 to the number of nodes with

accessed counts > 1 (i.e., at least accessed by two different graph

samples) and simplify the problem to a 1-dimensional linear search

on the segment size. This is equivalent to storing all node features

in the disk cache since node features with accessed count = 1 will

always be grouped together, in the same manner as packed fea-

tures. Furthermore, we use the storage consumption and I/O traffic

of a segment to estimate the total results of an epoch, by using a

scaling factor. This enables us to read each graph sample from disk

only once with the growth of the segment size. The search termi-

nates when the estimated disk consumption satisfies the constraint.

Experimental results in §7 show that the proposed approximate

method can speed up the search time at a significant scale while

providing near-optimal configurations for 𝑚 and 𝑠.

Page 7

5.2 Batched Feature Packing

During the pre-processing stage of DiskGNN, we need to fetch node

features from their original on-disk layout and write them back

to contiguous disk space as packed feature chunks. However, this

involves random reads to on-disk features, incurring high I/O cost

due to read amplification and thus long pre-processing overhead.

Next, we first present a naive design that extracts and packs features

for one mini-batch at a time. We then discuss how DiskGNN turns

random reads to sequential reads with batched feature packing.

Individual packing. The naive method processes each mini-batch

independently, as shown in Figure 6a. Specifically, it does random

reads to fetch a mini-batch’s required node features from the disk at

each iteration. It faces two performance issues. First, each random

read is small as it only fetches a single node’s feature, resulting

in I/O amplification. Second, for nodes that appear in multiple

mini-batches, their features are read from the disk multiple times,

resulting in redundant I/O. Consequently, the individual packing

approach has poor I/O efficiency. As shown in Figure 14, it brings

an extra 31% overhead for training an epoch with DiskGNN when

amortizing the pre-processing time to every epoch.

Batched packing. To eliminate I/O amplification and duplicate

reads, we switch to performing packing for one feature partition

at a time instead of one mini-batch at a time. We call the resulting

solution, batched packing method, because it simultaneously packs

features for all mini-batches. Specifically, we split the node features

of the entire graph into a few logical partitions so that each of which

fit into the CPU memory. At each iteration, we load a partition of

consecutive node features from disk, perform feature packing for

all graph samples within this partition in memory, and write back

the partial output to disk.

Figure 6b gives an example of batched packing. At the first itera-

tion, the feature partition containing node {0—5} is loaded using a

single sequential read of three underlying SSD pages with no read

amplification. Nodes within the loaded feature partition are needed

by different graph samples. For instance, graph sample 𝐺0 requires

nodes {0, 2} and𝐺𝑛 requires nodes {2, 5}) from the feature partition

{0—5}. We write the required node contiguously in memory for

each graph sample. The dispatch of node features to different graph

samples can be fully parallelized without any need for synchro-

nization. After gathering the required node features for the current

partition, we perform a sequential write for each graph sample to

append its partial feature chunk to the graph’s packed feature file

on disk. Once all the feature partitions have been processed, each

graph’s packed feature file is complete on disk. In practice, because

each graph sample can typically gather more features than that fit

in a 4KB disk page, the sequential writes are large, thereby avoiding

write amplification and improving I/O efficiency.

Batch packing effectively addresses the two major drawbacks

of individual packing by replacing small random reads with large

sequential reads, and ensuring that each node feature is loaded

only once throughout the process. We point out that the batched

packing method can also be parallelized across machines to gain

further speedup, as there are no data dependencies during packing

across different feature partitions. However, this is out of the scope

of this project and we leave it to future work.

Graph

Loader

+ + +

Feature

Loader

Feature

Assembler

Model

Trainer

Partial Feat. Queue

Complete

Feat. Queue

Graph Queue

Figure 7: DiskGNN’s training pipeline.

5.3 Pipelined Training

Naive execution that trains each mini-batch by first fetching its

constituent node features from disk results in low CPU and GPU

utilization during I/O access. Like other out-of-core training sys-

tems [20, 36], we leverage a pipelined design to overlap the compu-

tation and I/O access of adjacent mini-batches. The key difference

from existing systems [20, 36] is that DiskGNN uses more fine-

grained pipeline stages to handle the loading and assembly of node

features from our four-level feature store.

The pipelined procedure is presented in Figure 7. DiskGNN di-

vides the work required to train a mini-batch into four pipeline

stages, each of which is handled by a worker thread. Workers ad-

here to the producer-consumer pattern using shared queues to

execute the different stages of consecutive mini-batches in parallel.

The four pipeline stages consist of: 1 feature loading 2 feature

assembling 3 graphloading 4 modeltraining.

We note that four pipeline stages are non-linear with two sep-

arate dependency paths: 1 → 2 → 4 and 3 → 4 . On the first

pipeline path, feature loader starts by fetching the on-disk features

into consecutive CPU memory from disk cache and packed feature

chunks. The fetched features form a partial set of a mini-batch’s

node features and are put into the queue for the next pipeline stage.

Then the worker, feature assembler, assembles features from both

the CPU and GPU memory to form a mini-batch’s full set of node

features in one CUDA kernel. To do so, it performs direct memory

accesses to the GPU cache and UVA access to the CPU cache as well

as the mini-batch’s fetched partial features in the CPU memory.

After assembling, the complete features of a mini-batch are sent

to the complete-feature-queue. On the second pipeline path, graph

loader fetches the graph topology of each mini-batch from disk to

the graph-queue in CPU memory. As the last stage for both pipeline

paths, model trainer retrieves both the graph sample and the com-

plete features of a mini-batch from the two corresponding queues,

and conducts GNN training computation for this mini-batch. By

using the same ordering to process mini-batches in both pipeline

paths, DiskGNN guarantees that heads of both the complete-feature-

queue and the graph-queue belong to the same mini-batch. As the

GNN model and graph samples are typically small and can not

saturate the GPU, we put model trainer and feature assembler on

separate CUDA streams to improve GPU utilization.

With pipelining, the four worker threads can run tasks concur-

rently to work on different mini-batches. For example, when model

trainer is performing GNN computation on mini-batch 𝑛, graph

loader can fetch the graph sample for mini-batch 𝑛+1, feature as-

sembler can assemble the complete features for mini-batch 𝑛+1, and

feature loader can load the on-disk features for mini-batch𝑛+2. This

Page 8

allows for the overlap of SSD reads, CPU to GPU data transfer, and

GPU computation so that the overall performance is bottlenecked

by the longest stage as opposed to the sum of all stages.

6 IMPLEMENTATION

DiskGNN is developed using the C++ library of Pytorch [37] as the

backend. We utilize DGL [22], a popular open-source framework for

graph learning, to store graph samples in disk and perform model

training. The implementation considerations consist of three parts:

• I/O operations: DiskGNN uses pread[38] for sequential disk ac-

cess to fetch packed feature chunks, which can fully saturate the

SSD bandwidth with a single thread. For random disk accesses

to fetch features in disk cache, DiskGNN leverages io_uring [39]

and uses 4 threads with each thread holding a ring to launch

I/O requests, which is observed to have nearly-saturated I/O

performance. OpenMP[40] is used to execute that 4 threads and

subsequent memcpy operations in parallel. For all I/O operations,

we use POSIX open [41] as the file descriptor with O_Direct flag

to bypass the OS page cache and directly access the SSD.

• Feature assembling: For feature assembling in GPU, DiskGNN

uses Unified Virtual Addressing (UVA) [35] in GPU kernels to

fetch features resident on CPU main memory. For the address

lookup operations both in CPU and GPU to generate correspond-

ing feature locations, we prepare the interpreted address loca-

tions during pre-processing and directly load them from disk

during online training. This saves the interpreting cost in the

training pipeline and avoids duplicated address generation for

each graph sample across epochs.

• Training pipeline: For the producer-consumer-based pipelining

of DiskGNN, each shared queue is set to have a size limit of 2,

which can fully overlap the stages while not causing too much

extra memory occupation.

Easy-to-use API. DiskGNN has API that offers users an exception-

ally simple interface, enabling them to build efficient data layout

and conduct training based on their disk size budget with just a

single line of Python code.

def DiskGNN_train(dataset_pth : PATH, disk_size : int, cpu_size :

int, gpu_size : int, kwargs)

↩→

DiskGNN initially arranges the data layout for the CPU and GPU

cache using cpu_size and gpu_size, respectively. Then, a lightweight

search algorithm is employed to identify the data layout for seg-

mented disk cache under the constraint of disk_size. Subsequently,

batched packing is used to efficiently pack on-disk features. After

completing all data orchestration, training can start by integrating

the I/O engine, feature assembler, and model trainer.

7 EVALUATION

In this part, we conduct extensive experiments to evaluate DiskGNN

and compare them with state-of-the-art disk-based GNN training

systems. The main observations are that:

• DiskGNN consistently yields shorter training time than the base-

lines while matching the best model accuracy of them.

• DiskGNN performs well across different configurations.

• The designs of DiskGNN, e.g., node reordering, batched packing,

and training pipeline, are effective in improving efficiency.

Table 2: Graph datasets used in the experiments.

Attributes

Friendster Papers MAG240M IGB260M

Abbr.

Vertex count

66M

111M

244M

269M

Edge count

3.6B

3.3B

3.4B

3.9B

Graph size (GB)

28.5

25.9

27.9

30.8

Feature size (GB)

31.3

52.9

117

129

Train nodes (%)

N/A

1.09

0.45

5.06

Table 3: Model accuracy (%) comparison for the systems.

Systems

Datasets

Papers100M MAG240M IGB-HOM

SAGE

Ginex

65.85

67.90

58.96

DiskGNN

65.91

67.79

59.03

MariusGNN

64.01

65.50

58.77

GAT

Ginex

65.03

66.48

56.43

DiskGNN

65.03

66.53

56.69

MariusGNN

OOM

7.1 Experiment Settings

Datasets and models. We use the four graph datasets in Table 2

for experiments and refer to them by abbreviations subsequently.

These datasets are publicly available and widely used to evaluate

GNN models and systems. Since FS and IG are undirected graphs,

we replace each undirected edges with two directed edges. For

PS, we add a reverse edge for each directed edge to enlarge the

receptive field of each node during neighbor aggregation. As FS

only provides the graph topology (i.e., without node features and

labels), we randomly generate a 128-dimension float vector for each

node as the feature and select 1% of its nodes as the seed nodes for

training by assigning fake labels.

We choose two representative models, i.e., GraphSAGE [23] and

GAT [42], and adopt their popular hyper-parameter settings. In

particular, GraphSAGE uses the mean aggregation function while

GAT uses multi-head attention for neighbor aggregation. The hid-

den embedding dimension of GraphSAGE is 256 while a hidden

embedding dimension of 32 and 4 attention heads are used for GAT.

Following the open-source example from DGL [43], both Graph-

SAGE and GAT are set to have 3 layers. For graph sampling, we

use node-wise neighbor sampling with a fanout of [10,15,20], and

the number of seed nodes (i.e., batch size) in a mini-batch is 1024.

Baseline systems. We compare DiskGNN with two state-of-the-

art disk-based GNN training systems, i.e., Ginex [18] and Marius-

GNN [20]. As introduced in § 1, Ginex accesses each node feature

individually and manages the nodes cached in CPU memory using

the Belady’s algorithm; MariusGNN organizes the graph into edge

chunks and node partitions and samples only the memory-resident

node features for training. We do not compare with Helios [21]

and GIDS [19] because Helios is not open-sourced, and GIDS uses

GPU-initiated disk I/O, which is only supported by the latest GPUs.

They both adopt the fine-grained feature access of Ginex and thus

also suffer from the read amplification problem. As a naive baseline,

Page 9

Ogbn-papers100M

Normaliz

Runtime

DiskGNN

DiskGNN+Preprocess

MariusGNN

MariusGNN+Preprocess

Ginex

Ginex+Sample

DGL-OnDisk

1.0 1.03

2.69

3.46

7.61

54.57 72.39

MAG240M

1.0 1.04

3.12

5.26

8.42

58.42 86.53

Friendster

1.0 1.04 1.07

1.65

8.76

26.34 38.13

IGB-HOM

1.0 1.03

1.6 1.91

7.95

29.81

N/A

(a) GraphSAGE

Ogbn-papers100M

Normaliz

Runtime

1.0 1.02

5.2

5.83 6.07

45.42 60.86

MAG240M

1.0 1.03

2.79

4.44

6.76

45.29 66.99

Friendster

1.0 1.03 1.39

1.94

7.82

23.56 34.25

IGB-HOM

1.0 1.03

1.83 2.02

6.85

27.32

N/A

(b) GAT

Figure 8: Normalized epoch time for training the two GNN models, the epoch time of DiskGNN is set as 1.0 in each case.

we also altered DGL for disk-based training (called DGL-OnDisk)

by directly using pread to read the node features from disk.

Platform and metrics. We conduct the experiments on a AWS

g5.48xlarge [44] instance with a 96-core AMD EPYC 7R32 CPU,

748GB RAM, 2 × 3.8TB NVMe SSD, and an NVIDIA A10G GPU

with 24GB memory. To simulate the case of large graphs that ex-

ceed CPU memory, we set memory constraints for the systems as

different proportions of the graph features, with 10% by default.

The NVMe SSD of the machine provides 2.5GBps bandwidth and

625,000 IOPS at the maximum. The GPU is connected to the host

CPU via PCIe 3.0 with a full bandwidth of 7GBps. The operating

system is Ubuntu 20.04, and the software is CUDA 11.7 [45], Python

3.9.18 [46], PyTorch 2.0.1 [47], DGL 1.1.2 [43], and PyG 2.5.0 [48].

We compare the systems in terms of both model accuracy and

training efficiency. For model accuracy, we report the test accuracy

at the epoch when the highest validation accuracy is achieved for

each system. For PS and MG, we run 50 epochs of training to reach

convergence. For IG, only 20 epochs are needed as it has more seed

nodes. We do not use FS in accuracy evaluation because it does

not provide node features and labels. For training efficiency, we

run each system for 5 epochs and record the average time of the

latter 4 epochs, leaving the first epoch for warning up. The default

CPU memory is set as 10% of the graph feature size to simulate the

case of large graphs that exceed CPU memory. We also adjust the

memory constraint to check its influence on the epoch time.

7.2 Main Results

Model accuracy. Table 3 reports the model accuracy achieved by

the systems. For PS and IG, the accuracy is measured on the test

set, while MG uses the validation set as it only provides labels

for the validation set. The results show that DiskGNN matches

the model accuracy of Ginex for both GraphSAGE and GAT, and

the small differences may be caused by random factors such as

parameter initialization and random graph sampling. However, the

model accuracy of MariusGNN is noticeably lower than Ginex and

DiskGNN. For instance, for the PS graph and GraphSAGE model,

the accuracy degradation of MariusGNN is 2.4%, which is large

for GNN models and may be unacceptable for applications such

as recommendation. For GAT, MariusGNN runs out-of-memory

(OOM) when evaluating the model accuracy for all datasets.

Epoch time. Figure 8 reports the epoch time of the systems. In

each case (i.e., model plus dataset), we normalize the results by

treating the epoch time of DiskGNN as 1 because the epoch time

spans a large range for different datasets, which will make the figure

difficult to read if we use real epoch time. Note that the results of

DiskGNN, Ginex, and Marius do not include their pre-processing

time. To understand the influence of pre-processing, we also report

DiskGNN + preprocess, Marius + preprocess, and Ginex + sample,

which amortize the pre-processing time of the systems over the

training epochs. During pre-processing, Ginex conducts disk-based

graph sampling while Marius organizes the graph into edge chunks

and node partitions. DGL-OnDisk can not finish an epoch in 10

hours on IG, and thus we report N/A for it.

Figure 8 shows that DiskGNN consistently outperforms all base-

line systems across the four datasets and two GNN models. In partic-

ular, the speedup of DiskGNN over Ginex is over 6x in all cases and

can be 8.76x at the maximum. This is because Ginex suffers from

severe read amplification by reading each node feature individually

from disk while DiskGNN enjoys efficient disk access due to its data

layout optimizations. Compared with MariusGNN, DiskGNN has

about 2x speedup in 6 out of the 8 cases, while providing superior

model accuracy. The speedup on FS is smaller because the node

access for FS is more uniform with the popular nodes being less

Page 10

10%

30%

50%

Ogbn-papers100M

Normaliz

Runtime

DiskGNN

MariusGNN

Ginex

1.0

0.59

0.51

2.69

4.33

7.51

7.05

6.79

6.35

10%

30%

50%

MAG240M

1.0

0.61

0.6

3.12

6.01

10.62

8.43

8.29

8.22

10%

30%

50%

Friendster

Normaliz

Runtime

1.0

0.38

0.29

1.07

2.57

5.28

8.73

3.53

2.99

10%

30%

50%

IGB-HOM

1.0

0.39

0.34

1.6

2.17

2.83

7.95

4.84

4.79

Figure 9: Normalized epoch time with different CPU memory

constraints for training the GraphSAGE model.

dominant, which makes the CPU and GPU cache less effective. Re-

garding DGL-OnDisk, the speedup of DiskGNN is significant (i.e.,

86.53x at the maximum) because DGL-OnDisk needs to read every

node feature from disk (without CPU and GPU cache), suggesting

that a naive solution is inefficient. Considering the pre-processing

time, the difference between DiskGNN + preprocess and DiskGNN

is within 5% for all cases, indicating the pre-processing of DiskGNN

is efficient. The pre-processing of MariusGNN also does not add too

much overhead but Ginex has a long pre-processing time because

disk-based graph sampling is slow.

Memory constraint. Figure 9 compares the epoch time of DiskGNN,

MariusGNN, and Ginex for training the GraphSAGE model. We ad-

just the cache memory the systems can use as percentages (i.e., 10%,

30% and 50%) of the node feature size of the datasets. The results

show that DiskGNN trains consistently faster than MariusGNN and

Ginex with different memory constraints, achieving a maximum

speedup of 10.62x and 8.73x, respectively. DiskGNN observes a

diminishing return in training efficiency when enlarging the cache

memory, i.e., increasing from 30% to 50% feature size brings a much

smaller speedup than from 10% to 30% feature size. This is because

most accesses to node features are served by the GPU and CPU

caches with a reasonable memory size, and thus further increasing

the memory is not very effective in reducing disk access.

MariusGNN has longer epoch time at larger memory size because

it loads more edge chunks and node partitions from disk to fill in the

CPU memory. Moreover, graph sampling for the on-CPU partitions

will involve more neighbors, leading to longer sampling time and

model training time as the graph samples involve more edges. We

note that loading more partitions enables MariusGNN to achieve

higher model accuracy but its accuracy is still lower than Ginex

and DiskGNN. For instance, with the memory being 50% of the

feature size, MariusGNN improves the model accuracy by 0.71%

(from 64.01% to 64.72%) on the PS graph but the degradation from

Ginex and DiskGNN (i.e., 65.85% and 65.91%) is still significant.

10%

30%

50%

Friendster

100

101

102

103

104

Read

I/O

(GB

)

DiskGNN

MariusGNN

Ginex

168.84

50.5

17.56

3.82

11.46

19.09

997.38

290.22

108.32

10%

30%

50%

IGB-HOM

1346.89

19.13

0.0

15.68

47.04

78.39

7294.17

130.74

0.0

Figure 10: Disk traffic adjusting the CPU memory constraints.

Friendster

Normaliz

Runtime

Unlimited

Ginex

1.0

1.13

2.72

8.76

IGB-HOM

1.0

2.18

3.33

4.92

7.95

Figure 11: Normalized epoch time with different disk space

constraints for training the GraphSAGE model.

Disk traffic. To understand the results in Figure 9, Figure 10 report

the average amount of data the systems read from disk in an epoch.

We include only FS and IG due to the page limit (similarly for some

other experiments). Note that the disk traffic results are the same

for GraphSAGE and GAT. The results show that the disk traffic of

DiskGNN is never above 1/5 of Ginex under all cache configurations

and datasets, which explains the speedup of DiskGNN over Ginex.

When the memory size is 50% of the node features, DiskGNN and

Ginex almost have no disk traffic to read the node features on

the IG graph, justifying the diminishing return of increasing CPU

memory. As we have explained, MarisGNN has larger disk traffic

with larger CPU memory because it loads more node partitions and

edge chunks to fill in the CPU memory.

Disk space constraint. Figure 11 reports the epoch time of DiskGNN

with different constraints on the disk space DiskGNN uses, which

is specified as the times (i.e., 7x, 5x and 3x) of the feature size. We

include Ginex as a reference, and the results of PS and MG are

omitted because they use less disk space than the original feature

size even if we only use packing. When disk space is unlimited, FS

uses 5.39x of the feature size while IG uses 10.19x of the feature size.

This explains why FS observes a very small change in epoch time

when switching from unlimited to 5x feature size. The results show

that DiskGNN has a longer training time when using smaller disk

space. This is because DiskGNN needs to store more node features

in the disk cache instead of packed feature chunks, and the disk

cache relies on node reordering to reduce read amplification while

packed feature chunks eliminate read amplification.

Disk consumption. The maximum disk space DiskGNN can use

is affected by the cache memory size because when the GPU and

Page 11

10%

15%

20%

30%

50%

70%

Cache Sizes

Disk

Siz

Blo

wup

Friendster

IGB-HOM

5.4

4.42

2.96

1.61

0.56

0.16

10.49

4.93

1.93

0.15

0.02

Figure 12: Disk space usage under different CPU cache sizes.

10M

15M

20M

Number of Disk Cache Entries

I/O

raffic

Amplification

100

150

w/o Reorder

w/ Reorder

Blowup

10M

15M

20M

Number of Disk Cache Entries

Disk

Space

Blo

wup

Figure 13: Effectiveness of segmented disk cache on the FS

graph. Left is the disk traffic amplification for feature read-

ing, right is the disk space usage w.r.t. the original features.

CPU cache hold more node features, fewer node features need to be

stored in the packed feature chunks on disk. Figure 12 reports how

the maximum disk space usage changes when adjusting the cache

memory size, and both disk space and cache memory are relative

to the size of the node features. The results show that the disk

space usage reduces quickly when increasing the cache memory

size. Specifically, we observe that only about 20% memory cache

ratio is needed to keep the disk space consumption below 2 times

of the original features. The reduction of the disk space usage is

more significant on the IG graph because its node accesses are more

skewed towards the popular nodes and thus the node feature cache

is more effective in reducing disk access.

7.3 Micro Experiments

Segmented disk cache with reordering. In Figure 13, we evaluate

the effectiveness of the segmented disk cache and node reordering

on the FS graph. We use three configurations (i.e., 50, 100, and 150)

for the number of min-batches in a segment (i.e., segment size). The

results of the other graphs are similar and omitted due to the space

limit. The left plot of Figure 13 suggests that our MinHash-based

node reordering is effective in reducing the disk traffic across differ-

ent segment sizes. Moreover, the disk traffic reduction of reordering

is larger with a smaller segment size (e.g., 50 vs 150). This is because

the feature reordering suits the min-batches better when consider-

ing a small number of mini-batches. The right plot shows that using

a smaller segment size constantly yields a larger disk space con-

sumption. Moreover, the disk consumption has a lower bound for a

specific segment size and the minimum value is achieved by storing

Table 4: Efficiency and quality of the approximate method

to search for disk cache configuration compared with the

brutal-force search. Blowup is the disk space to use, I/O Amp.

is disk traffic amplification, and Time is the search time.

Blowup

Methods

Speedup

Brutal Force

Approximate

I/O Amp. Time (s) I/O Amp. Time (s)

2.98x

257.0

2.58x

0.99

259.60x

1.33x

75.00

1.09x

0.18

416.67x

5.10x

6288

4.85x

22.8

275.83x

4.11x

4755

3.39x

9.76

487.27x

3.01x

3261

2.28x

4.61

707.52x

Ogbn-papers100M

MAG240M

Friendster

IGB-HOM

Normaliz

Runtime

Individual Packing

Batched Packing

Read Time

7.0

3.75

8.8

9.82

1.0

Figure 14: Running time of naive individual packing and our

optimization batched packing for pre-processing.

all node features in the disk cache. At this point, the disk space

consumption is the multiple segments of disk cache themselves,

which is smaller with a larger segment size. We also observe that

different segment sizes produce similar disk consumption when the

size of the disk cache is small, which validates the assumption of

our approximate search method for the configuration of disk cache

size and segment size.

Approximate search method. Table 4 compares the search time

and result quality of our approximate method to search for the

configuration of disk cache in § 5.1 with the brute-force search

that returns the optimal configuration. The resulting quality can

be measured by the disk traffic amplification, and lower amplifica-

tion means high quality. Brute-force search may have lower result

quality than our method because it needs a step size when iterating

over the segment size, as generating the disk cache and reordering

repeatedly for every segment size is too costly, and this prevents

brute-force search from finding the optimal configuration. Specifi-

cally, we set the step size for FS and IG as 100 and 1000 respectively,

which is roughly 1/10 of the total number of mini-batches. The

results show that our approximate method reduces the search time

of brute-force by over 250x while yielding comparing or even bet-

ter result quality, mainly by eliminating repeated graph loading,

disk cache generation, and local reordering. Moreover, the disk

consumption curve in Figure 13 also verifies the assumption that

disk consumption is almost the same under small disk cache sizes,

making it reasonable for us to do the approximation.

Batched packing. Figure 14 shows the speedup of batched packing

over individual packing across 4 datasets. For both solutions, we

Page 12

Table 5: Pipelined training vs. sequential execution.

CPU cache size

Methods

Speedup

Sequential Pipeline

10%

165.3

96.67

1.71x

30%

88.98

36.51

2.44x

50%

54.24

28.12

1.93x

10%

1901.82

960.73

1.98x

30%

652.35

312.71

2.09x

50%

641.26

324.61

1.98x

mark the read time during end-to-end preprocessing in the figure.

With individual packing, read time is consistently the bottleneck,

constituting between 81% and 90% of the total preprocessing time.

Batched packing addresses this bottleneck by replacing duplicated

random reads of on-disk features with sequential reads without

duplication. As shown in Figure 14, batched packing achieves an

average 7.34x speedup in total preprocessing time, especially with

an average 61x speedup for feature loading. The speedup on FS and

IG is generally larger since they need to load more features under

a more uniform access skewness and a low cache hit ratio. With

the significant optimization of batched packing, the overhead of

preprocessing becomes negligible for the entire training pipeline.

GNN training pipelining. Table 5 validates the effectiveness of

the producer-consumer-based GNN training pipeline. Sequential ex-

ecution refers to the implementation that uses only a single thread

to conduct graph loading, feature loading, feature assembling, and

model training, and pipelined execution conducts them in parallel.

Results show that the proposed stage division and pipelining gives

a maximal speedup of 2.44x and an average speedup of over 2x com-

pared with sequential execution. Under a small cache size ratio (e.g.,

10%), the training pipeline is bottlenecked by heavy disk feature

loading, whereas graph loading or model computation becomes the

dominant part when the cache size ratio is high (e.g., 50%).

8 RELATED WORK

Graph learning frameworks. DGL [22] and PyG [34] are two

popular deep learning frameworks on graphs, which provide com-

prehensive user interfaces to express various GNN algorithms and

efficient CPU and GPU operators for graph sampling and model

training. We enjoy these optimizations by building DiskGNN upon

them. On top of these two frameworks, there are many systems

focusing on GPU kernel optimizations during GNN training, such

as GNNadvisor [49], Graphiler [50], TC-GNN [51]. These optimiza-

tions are orthogonal to DiskGNN. Furthermore, they all assume

the graph topology and node features can fit in CPU main memory

and do not optimize the data loading from disk to CPU when graph

data is large enough, which is the main focus of DiskGNN.

Large-scale GNN training systems. To handle large-scale graphs

that can not fit in CPU main memory, many systems utilize multiple

machines to train the GNN model in parallel with each machine

holding a partition of graph data in CPU. NeuGraph [52], ROC [53],

PipeGCN [54], BNS-GCN [55] and DGCL [56] are representative

early distributed GNN systems but adopt full-graph training which

brings about high GPU memory consumption for intermediate

hidden embeddings and easily exceeds the GPU capacity when

scaling up. Recent systems adopt a sampling-based approach to

control the number of neighbors for aggregation [15–17, 57–60].

Though they can scale up GNN training, all these systems need a

cluster to hold the graph data, which is expensive in practice for

huge industrial graphs with tens of billions of nodes and edges. Also,

they suffer from heavy communication costs between machines to

exchange node features and intermediate embeddings.

Our-of-core DNN workload. There are also systems developed

for training large-scale DNN workloads on disk. FlexGen [61] ag-

gregates storage resources from the GPU, CPU, and disk, enabling

175B parameter model inference on a single GPU. Dragon [62] and

FlashNeuron [63] employ GPU-direct storage access to retrieve

intermediate data from NVMe SSD while minimizing interference

with applications running on the CPU. The techniques introduced

in these systems can not directly be applied to GNN training as

they all focus on the GPU capacity limit due to the huge amount

of parameters in DNN. While for training GNN models, which are

much smaller, the CPU memory is typically the concern as it can

not hold the entire input node features. Moreover, the data access

patterns in DNN training are different from those in GNN training.

9 CONCLUSION

We present DiskGNN, an efficient framework designed to support

large-scale GNN training, specifically tailored for scenarios where

the graph features exceed CPU memory capacity and require of-

floading to disk. DiskGNN incorporates offline sampling with fea-

ture packing to bridge the I/O efficiency and model accuracy in

one system, and adopts a four-layer hierarchical feature store to

reduce disk access. Several optimizations are proposed and inte-

grated in DiskGNN, including reordering on the segmented disk

cache to reduce I/O traffic with better data locality, batched packing

to alleviate the preprocessing overhead by transforming duplicated

random disk reads into sequential disk reads without duplication,

and pipeline training to overlap disk access with other operations.

Extensive experiments demonstrate that DiskGNN significantly

outperforms existing GNN training systems, showing an average

speedup of 8× over baseline systems.

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